Investigating the engineering properties of fiber-reinforced ultra-high performance self-compacting concrete and predicting its rheological properties using a hybrid neural network and RBF

Document Type : Research Article


Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran


This study investigates the rheological and mechanical properties of ultra-high performance fiber reinforced self-compacting concrete (UHPSCC) using garnet and basalt aggregates, microsilica, fly ash, nanosilica, and steel fibers. To reduce construction costs, two artificial neural networks (ANN-GA and RBF-NN) are used to predict UHPSCC properties and compared with laboratory results. The studied rheological properties include slump flow diameter, slump flow time, V-funnel test, and L-box test. The laboratory results show high compressive and tensile strength, and acceptable rheological properties within EFNARC acceptance range. Both neural networks demonstrate acceptable accuracy in predicting rheological properties, with ANN-GA having higher prediction accuracy. Understanding UHPSCC properties is essential for the construction industry, and the use of ANN-GA can save on costs while maintaining accuracy in predicting its properties.


Main Subjects

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